RestREco is a research initiative that approaches ecological restoration with a focus on resilience, rather than returning ecosystems to their original state.
The Dig Deeper study explored how the age of restoration, establishment type, and site management influence bacterial and fungal communities in soil. This was achieved by analysing both 16S and ITS sequencing data collected from 66 distinct sites. The analysis focused on three main aspects:
The sequencing data were processed using the QIIME 2
bioinformatics platform — a widely used tool for microbiome analysis.
Raw amplicon reads were denoised using the
DADA2 plugin, enabling accurate identification of
amplicon sequence variants (ASVs) with single-nucleotide
resolution. This method improves upon traditional OTU clustering by
enhancing precision.
After quality filtering and feature table construction, the pipeline proceeded to:
Both 16S rRNA gene sequencing (for bacteria and archaea) and ITS sequencing (for fungi) were included, providing a broad overview of microbial diversity across samples.
You can explore the full MultiQC report by clicking the image below:
This barplot presents the number of raw sequencing reads for each
individual sample prior to any quality control or filtering.
Most samples exhibit a relatively consistent sequencing depth. However,
a few samples display notably lower read counts, which could potentially
influence downstream analyses if not properly filtered or
normalised.
The dataset encompasses 66 distinct sites, each contributing five soil samples. These sites span a wide age range — from 1 year to over 100 years — providing a valuable gradient for ecological comparisons.
Each site’s five samples:
In addition, soil pH and electrical conductivity (EC) were measured for every sample to help characterise environmental conditions.
There are:
3 establishment types
4 management types, which can be applied individually or in combination
→ Some sites follow a single management approach, while others incorporate two, three, or all four.
Soil pH is a critical environmental parameter that influences
microbial community structure, nutrient availability, and overall
ecosystem function.
This section summarises the average pH values for each sampled site,
grouped by establishment type.
The bar chart below allows for easy comparison of mean pH across
sites.
Each bar is coloured according to the establishment method (e.g., seed
mix, natural regeneration, green hay), and by hovering over a bar, the
user can view the precise pH value for each site.
Note: To improve clarity, site names have been removed from the x-axis, but full details are available via the interactive tooltip.
Figure 2. pH Mean For Each Site
This section explores the variation in electrical
conductivity (EC) across study sites.
Electrical conductivity is a measure of the soil’s ability to conduct
electricity, often reflecting ion concentration and soil
salinity, which can influence microbial activity and nutrient
availability.
The plots below allow users to examine how EC differs depending on either the type of establishment or the age of the site.
Use the drop-down menu to switch between views. Hover over the bars for detailed site-specific values.
This plot shows the variation in electrical conductivity across sites, sorted by electrical conductivity.
This section illustrates the types of management practices
applied at each site, including cutting,
cattle grazing, sheep grazing, and
ploughing.
Each coloured bar indicates the presence of one or more management
strategies at a given site. Sites with multiple bars have undergone
combinations of practices, highlighting the complexity
and variation in land use across the study area.
Hover over each bar in the interactive plot to see the site name.
Figure 4. Management type for each site
This section examines how different management
practices — such as cutting, grazing by cattle or sheep, and
ploughing — influence two key soil properties: pH and
electrical conductivity (EC).
These soil characteristics can affect microbial communities by altering
nutrient availability, pH balance, and soil structure.
The visualisations below display the overall effect of each
management type individually. However, it is important
to note that potential interactions between management
types (e.g., cutting combined with grazing) are not accounted
for here.
Such interactions may play a significant role in shaping soil conditions
but were beyond the scope of this visual summary.
Use the drop-down menu to explore how each management type affects pH
or EC across all sampled sites.
Black dots represent the mean values for each management category.
Alpha diversity refers to the variety of organisms within a particular sample or environment. It reflects both richness—the number of distinct taxa—and evenness—how evenly individuals are distributed among those taxa. One of the most widely used measures for assessing alpha diversity is the Shannon index.
The Shannon index takes into account not only the number of species present, but also how evenly their abundances are distributed. A higher Shannon value generally indicates a more diverse and ecologically balanced community.
Another important metric is Faith’s Phylogenetic Diversity (Faith PD), which measures the total branch length of the phylogenetic tree that spans the species in a sample. Unlike the Shannon index, Faith PD incorporates evolutionary relationships, providing a phylogenetic perspective on diversity.
In the interactive plots below, we examine how both the Shannon index and Faith PD vary across different environmental and experimental conditions, separately for the 16S (bacteria and archaea) and ITS (fungi) datasets.
Just as with the 16S data, we computed the Shannon diversity index for the ITS dataset to assess fungal alpha diversity. The resulting boxplots allow us to explore how fungal diversity varies across different environmental or experimental conditions, offering a parallel view to that of bacterial and archaeal communities.
This analysis provides valuable insights into how fungal communities respond to factors such as establishment method, site age, or land management, complementing the microbial diversity picture captured by the 16S data.
To explore differences in microbial communities, we often rely on dimensionality reduction techniques such as Principal Coordinates Analysis (PCoA), visualised through Emperor plots. Two commonly used distance metrics in this context are Bray-Curtis and Weighted UniFrac.
While both metrics can reveal meaningful clustering and separation in microbial data, they capture complementary aspects of community structure.
The Bray-Curtis Emperor plot is a 3D visualisation of microbial community dissimilarities between samples, based on the Bray-Curtis distance. This distance metric quantifies how different two samples are in terms of species abundance, taking into account both presence/absence and relative abundances. It does not incorporate evolutionary relationships between features.
Using Principal Coordinates Analysis (PCoA), the high-dimensional Bray-Curtis distance matrix is projected into a lower-dimensional space—typically three axes—to capture the main patterns of variation across samples.
The Emperor plot is an interactive 3D tool developed for QIIME 2 that allows users to explore these PCoA results. Samples are represented as points in space, and their spatial proximity reflects ecological similarity:
This type of plot is particularly useful for identifying clustering by experimental groups—such as treatment, site, or timepoint—and for detecting patterns or gradients in microbial diversity.
Here is a link to the bray curtis emperor plot for more flexibility on QIIME2: Bray-Curtis Emperor Plot (16S)
In contrast, Weighted UniFrac incorporates both species abundance and phylogenetic relationships. It measures the dissimilarity between microbial communities by accounting for how much evolutionary history is shared between them, weighted by the relative abundance of taxa.
This makes Weighted UniFrac particularly useful when the evolutionary context is important, as it highlights not only which organisms are present and in what quantities, but also how closely related they are.
As with Bray-Curtis, Principal Coordinates Analysis (PCoA) is applied to the distance matrix, and the results are displayed using an interactive Emperor plot. This allows for intuitive exploration of patterns in microbial composition, helping to reveal whether certain groups cluster together based on phylogenetic similarity and experimental conditions.
Here is a link to the weighted unifrac emperor plot for more flexibility on QIIME2: Weighted Unifrac Emperor Plot (16S)
To explore the composition of soil microbial communities, we used Krona plots — interactive, circular charts that display taxonomic abundances in a hierarchical manner.
These plots allow users to intuitively navigate from broader taxonomic levels (such as Phylum) to more specific ones (like Genus), while simultaneously comparing relative abundances across taxa.
In this study, Krona plots provide a powerful and user-friendly way to:
You can click on the image below to access the Krona plots for each site.
In microbial ecology, a guild refers to a group of organisms that fulfil similar ecological roles, regardless of their taxonomic identity. Understanding functional guilds allows researchers to move beyond taxonomic profiles and assess the ecological roles that microbial communities may play in an environment.
To investigate the ecological roles of fungal communities, we used FUNGuild, a tool that assigns fungi to functional guilds based on curated databases and literature. These guilds represent ecological strategies such as:
This functional classification provides valuable insights into what fungi are likely doing in the ecosystem, beyond simply who they are.
The plot below highlights the top 20 most abundant fungal guilds identified using FUNGuild. To avoid clutter, the guild names are hidden on the axis; however, users can hover over each bar to reveal the full name, enabling interactive and detailed exploration of fungal functional diversity.
Here you’ll find all the content that was given throughout the report : Bray-Curtis Emperor Plot (16S) Weighted Unifrac Emperor Plot (16S) Krona Plots by Site (16S)